105 citations · 120 across the 5 of their papers we have counts for
5 papers
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea +2
Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in…
Atomistic graph networks for experimental materials property prediction
Tian Xie, Victor Bapst, Alexander L. Gaunt +5
Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Mat…
GraphHop: An Enhanced Label Propagation Method for Node Classification
Tian Xie, Bin Wang, C. -C. Jay Kuo
A scalable semi-supervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains attributes of all nodes but labels of…
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Chaoyang He, Tian Xie, Yu Rong +4
Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more…
Domain Representation for Knowledge Graph Embedding
Cunxiang Wang, Feiliang Ren, Zhichao Lin +3
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However,…